In recent work, artificial intelligence (AI)-driven frameworks in collaboration with neuroscience have proven to overcome challenges in Autism spectrum disorder (ASD) research. Autism presents a unique challenge due to its complexity, variability, and wide range of comorbidities that have room to explore brain-behavior relationships in individuals by utilizing AI. This chapter presents an AI-based framework for individuals with ASD using large-scale, open-access datasets from the Autism Brain Imaging Data Exchange (ABIDE). Leveraging functional MRI and phenotypic data, we apply connectivity and comorbidity analyses to understand better how neural activity patterns relate to behavioral traits and coexisting conditions. The development of computational models that reconstruct high-resolution, heatmap-like plots from fMRI scans and associated metadata enables the visual interpretation of spatial patterns in brain activity by applying machine learning and AI algorithms. Combining brain imaging and behavioral data, the framework supports more interpretable models of ASD heterogeneity. It provides insights into the basic neural mechanisms that may highlight key ASD traits and comorbid descriptions. This work reflects how mind–machine collaboration can enhance personalized understanding of neurodevelopmental conditions, contributing tools that bridge clinical background with computational modeling and digital twin approaches.